Reproducibility of tract segmentation between sessions using an unsupervised modelling-based approach
Reproducibility of tract segmentation between sessions using an unsupervised modelling-based approach
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DOI：
10.1016/j.neuroimage.2008.12.010
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发表时间：
2009-04-01
期刊：
影响因子：
5.7
通讯作者：
Bastin, Mark E.
中科院分区：
文献类型：
--
作者：
Clayden, Jonathan D.;Storkey, Amos J.;Bastin, Mark E.
这项工作描述了使用概率形状建模方法分割的白质束内测量的标量水扩散参数的可重复性分析。与先前报道的邻域神经束成像（NT）工作一样，该技术通过将使用许多候选点与参考神经束相匹配而生成的神经束进行纤维跟踪，从而优化了种子点的放置。参考神经束来自本研究中的白质图谱。对纤维跟踪结果没有直接的约束。期望最大化算法用于完全自动化过程，并且比以前的NT方法更有效地使用数据。然后使用随机效应模型分离各向异性分数和平均扩散率的主体内和主体间方差。我们发现测试-重测变异系数（cv）与另一项使用里程碑引导的单种子点的研究报告相似；以及类似于基于约束的多重ROI方法的受试者cv。我们得出的结论是，我们的方法至少与使用牵道造影进行牵道分割的其他方法一样有效，同时也有一些额外的好处，例如它为每个分割提供了匹配度度量。(c) 2008爱思唯尔公司版权所有。
This work describes a reproducibility analysis of scalar water diffusion parameters, measured within white matter tracts segmented using a probabilistic shape modelling method. In common with previously reported neighbourhood tractography (NT) work, the technique optimises seed point placement for fibre tracking by matching the tracts generated using a number of candidate points against a reference tract, which is derived from a white matter atlas in the present study. No direct constraints are applied to the fibre tracking results. An Expectation-Maximisation algorithm is used to fully automate the procedure, and make dramatically more efficient use of data than earlier NT methods. Within-subject and between-subject variances for fractional anisotropy and mean diffusivity within the tracts are then separated using a random effects model. We find test-retest coefficients of variation (CVs) similar to those reported in another study using landmark-guided single seed points; and subject to subject CVs similar to a constraint-based multiple ROI method. We conclude that our approach is at least as effective as other methods for tract segmentation using tractography, whilst also having some additional benefits, such as its provision of a goodness-of-match measure for each segmentation. (c) 2008 Elsevier Inc. All rights reserved.